The Feature-based Time Series Classification Algorithm: Tracking Differentiator-based Multiview Dilated Characteristics
In industrial production, obtaining the discrete status of equipment from massive time series data has become an urgent demand, which places higher requirements on the time series classification (TSC) algorithm. The feature-based TSC method achieves interpretable and potential classification capability by extracting meaningful descriptive statistical features. Limitations exist in current feature-based TSC algorithms, hindering the acquisition of diverse statistical features due to a lack of knowledge and insufficient research on processing redundant features, thereby limiting performance enhancements. Therefore, in this article, a novel feature-based TSC algorithm tracking differentiator-based multiview dilated characteristics (TD-MVDC) is proposed. The innovative introduction of a tracking differentiator combined with dilation mapping as a preprocessor into the feature-based TSC method is proposed to improve feature diversity efficiently. Ensemble feature selection based on filter feature selectors with different store ratios is designed to generate multiview features to enhance feature stability quickly. Linear classifiers and hard voting to fastly classify and integrate multiview features to increase classification performance robustly. Finally, comparative and ablative experiments are conducted between TD-MVDC and the representative feature-based TSC algorithms on the extensively compared UCR archive to verify the effectiveness of the proposed algorithm.
[1] Changchun He, and Xin Huo. "Tracking Differentiator-based Multiview Dilated Characteristics for Time Series Classification." in The 22nd IEEE International Conference on Industrial Informatics (INDIN2024) (2024).
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